Adaptive quantization plays a critical role at enhancing perceptual video quality in video encoding. The sensitivity of the Human Visual System (HVS) varies according to the local content of the video. That is, finer quantization is applied in regions where HVS is more sensitive, and coarser quantization is applied where HVS is less sensitive. The sensitivity of HVS may be characterized by various features such as motion, texture, contrast, etc.
According to a typical procedure of adaptive quantization, for a particular feature of the video content, an index corresponding to a region such as a macro-block (MB) is first calculated. The index value indicates how much the HVS is sensitive to the content of the region. At a second step, the refinement of a quantization parameter (QP), called delta QP, for the region, is determined using this index, based on a model on this index. In a third typical step, the delta QP is added to the previously set QP, which may be a global QP flat at the frame level or the slice level. Subsequently, with the revised QP, the target quantization is performed for the region.
The effectiveness of such a solution, however, relies on many factors, including but not limited to, the selection of a feature and the calculation of its index, the mapping of the delta QP from the feature index, and the control of a bit-rate change due to the QP adjustment.